To construct a matrix efficiently, use either dok_matrix or lil_matrix.
The lil_matrix class supports basic slicing and fancy indexing with a
similar syntax to NumPy arrays. As illustrated below, the COO format
may also be used to efficiently construct matrices. Despite their
similarity to NumPy arrays, it is strongly discouraged to use NumPy
functions directly on these matrices because NumPy may not properly convert
them for computations, leading to unexpected (and incorrect) results. If you
do want to apply a NumPy function to these matrices, first check if SciPy has
its own implementation for the given sparse matrix class, or convert the
sparse matrix to a NumPy array (e.g. using the toarray() method of the
class) first before applying the method.

To perform manipulations such as multiplication or inversion, first
convert the matrix to either CSC or CSR format. The lil_matrix format is
row-based, so conversion to CSR is efficient, whereas conversion to CSC
is less so.

All conversions among the CSR, CSC, and COO formats are efficient,
linear-time operations.

CSR column indices are not necessarily sorted. Likewise for CSC row
indices. Use the .sorted_indices() and .sort_indices() methods when
sorted indices are required (e.g. when passing data to other libraries).